txt2panoimg/pipeline_base.py

# Copyright © Alibaba, Inc. and its affiliates.
# The implementation here is modifed based on diffusers.StableDiffusionPipeline,
# originally Apache 2.0 License and public available at
# https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

import re
from typing import Any, Callable, Dict, List, Optional, Union

import torch
from diffusers import (AutoencoderKL, DiffusionPipeline,
                       StableDiffusionPipeline)

from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
try:
    from diffusers.models.autoencoders.vae import DecoderOutput
except:
    from diffusers.models.vae import DecoderOutput
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import logging, replace_example_docstring
from transformers import CLIPTokenizer

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import EulerAncestralDiscreteScheduler
        >>> from txt2panoimage.pipeline_base import StableDiffusionBlendExtendPipeline
        >>> model_id = "models/sd-base"
        >>> pipe = StableDiffusionBlendExtendPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
        >>> pipe = pipe.to("cuda")
        >>> pipe.vae.enable_tiling()
        >>> pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
        >>> # remove following line if xformers is not installed
        >>> pipe.enable_xformers_memory_efficient_attention()
        >>> pipe.enable_model_cpu_offload()
        >>> prompt = "a living room"
        >>> image = pipe(prompt).images[0]
        ```
"""

re_attention = re.compile(
    r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""",
    re.X,
)


def parse_prompt_attention(text):
    """
    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
    Accepted tokens are:
      (abc) - increases attention to abc by a multiplier of 1.1
      (abc:3.12) - increases attention to abc by a multiplier of 3.12
      [abc] - decreases attention to abc by a multiplier of 1.1
    """

    res = []
    round_brackets = []
    square_brackets = []

    round_bracket_multiplier = 1.1
    square_bracket_multiplier = 1 / 1.1

    def multiply_range(start_position, multiplier):
        for p in range(start_position, len(res)):
            res[p][1] *= multiplier

    for m in re_attention.finditer(text):
        text = m.group(0)
        weight = m.group(1)

        if text.startswith('\\'):
            res.append([text[1:], 1.0])
        elif text == '(':
            round_brackets.append(len(res))
        elif text == '[':
            square_brackets.append(len(res))
        elif weight is not None and len(round_brackets) > 0:
            multiply_range(round_brackets.pop(), float(weight))
        elif text == ')' and len(round_brackets) > 0:
            multiply_range(round_brackets.pop(), round_bracket_multiplier)
        elif text == ']' and len(square_brackets) > 0:
            multiply_range(square_brackets.pop(), square_bracket_multiplier)
        else:
            res.append([text, 1.0])

    for pos in round_brackets:
        multiply_range(pos, round_bracket_multiplier)

    for pos in square_brackets:
        multiply_range(pos, square_bracket_multiplier)

    if len(res) == 0:
        res = [['', 1.0]]

    # merge runs of identical weights
    i = 0
    while i + 1 < len(res):
        if res[i][1] == res[i + 1][1]:
            res[i][0] += res[i + 1][0]
            res.pop(i + 1)
        else:
            i += 1

    return res


def get_prompts_with_weights(pipe: DiffusionPipeline, prompt: List[str],
                             max_length: int):
    r"""
    Tokenize a list of prompts and return its tokens with weights of each token.

    No padding, starting or ending token is included.
    """
    tokens = []
    weights = []
    truncated = False
    for text in prompt:
        texts_and_weights = parse_prompt_attention(text)
        text_token = []
        text_weight = []
        for word, weight in texts_and_weights:
            # tokenize and discard the starting and the ending token
            token = pipe.tokenizer(word).input_ids[1:-1]
            text_token += token
            # copy the weight by length of token
            text_weight += [weight] * len(token)
            # stop if the text is too long (longer than truncation limit)
            if len(text_token) > max_length:
                truncated = True
                break
        # truncate
        if len(text_token) > max_length:
            truncated = True
            text_token = text_token[:max_length]
            text_weight = text_weight[:max_length]
        tokens.append(text_token)
        weights.append(text_weight)
    if truncated:
        logger.warning(
            'Prompt was truncated. Try to shorten the prompt or increase max_embeddings_multiples'
        )
    return tokens, weights


def pad_tokens_and_weights(tokens,
                           weights,
                           max_length,
                           bos,
                           eos,
                           pad,
                           no_boseos_middle=True,
                           chunk_length=77):
    r"""
    Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
    """
    max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
    weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
    for i in range(len(tokens)):
        tokens[i] = [
            bos
        ] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos]
        if no_boseos_middle:
            weights[i] = [1.0] + weights[i] + [1.0] * (
                max_length - 1 - len(weights[i]))
        else:
            w = []
            if len(weights[i]) == 0:
                w = [1.0] * weights_length
            else:
                for j in range(max_embeddings_multiples):
                    w.append(1.0)  # weight for starting token in this chunk
                    w += weights[i][j * (chunk_length - 2):min(
                        len(weights[i]), (j + 1) * (chunk_length - 2))]
                    w.append(1.0)  # weight for ending token in this chunk
                w += [1.0] * (weights_length - len(w))
            weights[i] = w[:]

    return tokens, weights


def get_unweighted_text_embeddings(
    pipe: DiffusionPipeline,
    text_input: torch.Tensor,
    chunk_length: int,
    no_boseos_middle: Optional[bool] = True,
):
    """
    When the length of tokens is a multiple of the capacity of the text encoder,
    it should be split into chunks and sent to the text encoder individually.
    """
    max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)
    if max_embeddings_multiples > 1:
        text_embeddings = []
        for i in range(max_embeddings_multiples):
            # extract the i-th chunk
            text_input_chunk = text_input[:, i * (chunk_length - 2):(i + 1)
                                          * (chunk_length - 2) + 2].clone()

            # cover the head and the tail by the starting and the ending tokens
            text_input_chunk[:, 0] = text_input[0, 0]
            text_input_chunk[:, -1] = text_input[0, -1]
            text_embedding = pipe.text_encoder(text_input_chunk)[0]

            if no_boseos_middle:
                if i == 0:
                    # discard the ending token
                    text_embedding = text_embedding[:, :-1]
                elif i == max_embeddings_multiples - 1:
                    # discard the starting token
                    text_embedding = text_embedding[:, 1:]
                else:
                    # discard both starting and ending tokens
                    text_embedding = text_embedding[:, 1:-1]

            text_embeddings.append(text_embedding)
        text_embeddings = torch.concat(text_embeddings, axis=1)
    else:
        text_embeddings = pipe.text_encoder(text_input)[0]
    return text_embeddings


def get_weighted_text_embeddings(
    pipe: DiffusionPipeline,
    prompt: Union[str, List[str]],
    uncond_prompt: Optional[Union[str, List[str]]] = None,
    max_embeddings_multiples: Optional[int] = 3,
    no_boseos_middle: Optional[bool] = False,
    skip_parsing: Optional[bool] = False,
    skip_weighting: Optional[bool] = False,
):
    r"""
    Prompts can be assigned with local weights using brackets. For example,
    prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.

    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.

    Args:
        pipe (`DiffusionPipeline`):
            Pipe to provide access to the tokenizer and the text encoder.
        prompt (`str` or `List[str]`):
            The prompt or prompts to guide the image generation.
        uncond_prompt (`str` or `List[str]`):
            The unconditional prompt or prompts for guide the image generation. If unconditional prompt
            is provided, the embeddings of prompt and uncond_prompt are concatenated.
        max_embeddings_multiples (`int`, *optional*, defaults to `3`):
            The max multiple length of prompt embeddings compared to the max output length of text encoder.
        no_boseos_middle (`bool`, *optional*, defaults to `False`):
            If the length of text token is multiples of the capacity of text encoder, whether reserve the starting and
            ending token in each of the chunk in the middle.
        skip_parsing (`bool`, *optional*, defaults to `False`):
            Skip the parsing of brackets.
        skip_weighting (`bool`, *optional*, defaults to `False`):
            Skip the weighting. When the parsing is skipped, it is forced True.
    """
    max_length = (pipe.tokenizer.model_max_length
                  - 2) * max_embeddings_multiples + 2
    if isinstance(prompt, str):
        prompt = [prompt]

    if not skip_parsing:
        prompt_tokens, prompt_weights = get_prompts_with_weights(
            pipe, prompt, max_length - 2)
        if uncond_prompt is not None:
            if isinstance(uncond_prompt, str):
                uncond_prompt = [uncond_prompt]
            uncond_tokens, uncond_weights = get_prompts_with_weights(
                pipe, uncond_prompt, max_length - 2)
    else:
        prompt_tokens = [
            token[1:-1] for token in pipe.tokenizer(
                prompt, max_length=max_length, truncation=True).input_ids
        ]
        prompt_weights = [[1.0] * len(token) for token in prompt_tokens]
        if uncond_prompt is not None:
            if isinstance(uncond_prompt, str):
                uncond_prompt = [uncond_prompt]
            uncond_tokens = [
                token[1:-1] for token in pipe.tokenizer(
                    uncond_prompt, max_length=max_length,
                    truncation=True).input_ids
            ]
            uncond_weights = [[1.0] * len(token) for token in uncond_tokens]

    # round up the longest length of tokens to a multiple of (model_max_length - 2)
    max_length = max([len(token) for token in prompt_tokens])
    if uncond_prompt is not None:
        max_length = max(max_length,
                         max([len(token) for token in uncond_tokens]))

    max_embeddings_multiples = min(
        max_embeddings_multiples,
        (max_length - 1) // (pipe.tokenizer.model_max_length - 2) + 1,
    )
    max_embeddings_multiples = max(1, max_embeddings_multiples)
    max_length = (pipe.tokenizer.model_max_length
                  - 2) * max_embeddings_multiples + 2

    # pad the length of tokens and weights
    bos = pipe.tokenizer.bos_token_id
    eos = pipe.tokenizer.eos_token_id
    pad = getattr(pipe.tokenizer, 'pad_token_id', eos)
    prompt_tokens, prompt_weights = pad_tokens_and_weights(
        prompt_tokens,
        prompt_weights,
        max_length,
        bos,
        eos,
        pad,
        no_boseos_middle=no_boseos_middle,
        chunk_length=pipe.tokenizer.model_max_length,
    )
    prompt_tokens = torch.tensor(
        prompt_tokens, dtype=torch.long, device=pipe.device)
    if uncond_prompt is not None:
        uncond_tokens, uncond_weights = pad_tokens_and_weights(
            uncond_tokens,
            uncond_weights,
            max_length,
            bos,
            eos,
            pad,
            no_boseos_middle=no_boseos_middle,
            chunk_length=pipe.tokenizer.model_max_length,
        )
        uncond_tokens = torch.tensor(
            uncond_tokens, dtype=torch.long, device=pipe.device)

    # get the embeddings
    text_embeddings = get_unweighted_text_embeddings(
        pipe,
        prompt_tokens,
        pipe.tokenizer.model_max_length,
        no_boseos_middle=no_boseos_middle,
    )
    prompt_weights = torch.tensor(
        prompt_weights,
        dtype=text_embeddings.dtype,
        device=text_embeddings.device)
    if uncond_prompt is not None:
        uncond_embeddings = get_unweighted_text_embeddings(
            pipe,
            uncond_tokens,
            pipe.tokenizer.model_max_length,
            no_boseos_middle=no_boseos_middle,
        )
        uncond_weights = torch.tensor(
            uncond_weights,
            dtype=uncond_embeddings.dtype,
            device=uncond_embeddings.device)

    # assign weights to the prompts and normalize in the sense of mean
    # TODO: should we normalize by chunk or in a whole (current implementation)?
    if (not skip_parsing) and (not skip_weighting):
        previous_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
            text_embeddings.dtype)
        text_embeddings *= prompt_weights.unsqueeze(-1)
        current_mean = text_embeddings.float().mean(axis=[-2, -1]).to(
            text_embeddings.dtype)
        text_embeddings *= (previous_mean
                            / current_mean).unsqueeze(-1).unsqueeze(-1)
        if uncond_prompt is not None:
            previous_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
                uncond_embeddings.dtype)
            uncond_embeddings *= uncond_weights.unsqueeze(-1)
            current_mean = uncond_embeddings.float().mean(axis=[-2, -1]).to(
                uncond_embeddings.dtype)
            uncond_embeddings *= (previous_mean
                                  / current_mean).unsqueeze(-1).unsqueeze(-1)

    if uncond_prompt is not None:
        return text_embeddings, uncond_embeddings
    return text_embeddings, None


def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(
        dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + (
        1 - guidance_rescale) * noise_cfg
    return noise_cfg


class StableDiffusionBlendExtendPipeline(StableDiffusionPipeline):
    r"""
    Pipeline for text-to-image generation using Stable Diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    In addition the pipeline inherits the following loading methods:
        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]
        - *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`]
        - *Ckpt*: [`loaders.FromCkptMixin.from_ckpt`]

    as well as the following saving methods:
        - *LoRA*: [`loaders.LoraLoaderMixin.save_lora_weights`]

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/
            en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """
    _optional_components = ['safety_checker', 'feature_extractor']

    def _encode_prompt(
        self,
        prompt,
        device,
        num_images_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        max_embeddings_multiples=3,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        lora_scale: Optional[float] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `list(int)`):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`str` or `List[str]`):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
                The max multiple length of prompt embeddings compared to the max output length of text encoder.
        """
        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
            self._lora_scale = lora_scale

        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if negative_prompt_embeds is None:
            if negative_prompt is None:
                negative_prompt = [''] * batch_size
            elif isinstance(negative_prompt, str):
                negative_prompt = [negative_prompt] * batch_size
            if batch_size != len(negative_prompt):
                raise ValueError(
                    f'`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:'
                    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                    ' the batch size of `prompt`.')
        if prompt_embeds is None or negative_prompt_embeds is None:
            if isinstance(self, TextualInversionLoaderMixin):
                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
                if do_classifier_free_guidance and negative_prompt_embeds is None:
                    negative_prompt = self.maybe_convert_prompt(
                        negative_prompt, self.tokenizer)

            prompt_embeds1, negative_prompt_embeds1 = get_weighted_text_embeddings(
                pipe=self,
                prompt=prompt,
                uncond_prompt=negative_prompt
                if do_classifier_free_guidance else None,
                max_embeddings_multiples=max_embeddings_multiples,
            )
            if prompt_embeds is None:
                prompt_embeds = prompt_embeds1
            if negative_prompt_embeds is None:
                negative_prompt_embeds = negative_prompt_embeds1

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt,
                                           seq_len, -1)

        if do_classifier_free_guidance:
            bs_embed, seq_len, _ = negative_prompt_embeds.shape
            negative_prompt_embeds = negative_prompt_embeds.repeat(
                1, num_images_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(
                bs_embed * num_images_per_prompt, seq_len, -1)
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    def blend_v(self, a, b, blend_extent):
        blend_extent = min(a.shape[2], b.shape[2], blend_extent)
        for y in range(blend_extent):
            b[:, :,
              y, :] = a[:, :, -blend_extent
                        + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (
                            y / blend_extent)
        return b

    def blend_h(self, a, b, blend_extent):
        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
        for x in range(blend_extent):
            b[:, :, :, x] = a[:, :, :, -blend_extent
                              + x] * (1 - x / blend_extent) + b[:, :, :, x] * (
                                  x / blend_extent)
        return b

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator,
                                  List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = 'pil',
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor],
                                    None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
                to make generation deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
            guidance_rescale (`float`, *optional*, defaults to 0.7):
                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                Guidance rescale factor should fix overexposure when using zero terminal SNR.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        def tiled_decode(
            self,
            z: torch.FloatTensor,
            return_dict: bool = True
        ) -> Union[DecoderOutput, torch.FloatTensor]:
            r"""Decode a batch of images using a tiled decoder.

            Args:
            When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several
            steps. This is useful to keep memory use constant regardless of image size.
            The end result of tiled decoding is: different from non-tiled decoding due to each tile using a different
            decoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output.
            You may still see tile-sized changes in the look of the output, but they should be much less noticeable.
                z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to
                `True`):
                    Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
            """
            _tile_overlap_factor = 1 - self.tile_overlap_factor
            overlap_size = int(self.tile_latent_min_size
                               * _tile_overlap_factor)
            blend_extent = int(self.tile_sample_min_size
                               * self.tile_overlap_factor)
            row_limit = self.tile_sample_min_size - blend_extent
            w = z.shape[3]
            z = torch.cat([z, z[:, :, :, :w // 4]], dim=-1)
            # Split z into overlapping 64x64 tiles and decode them separately.
            # The tiles have an overlap to avoid seams between tiles.

            rows = []
            for i in range(0, z.shape[2], overlap_size):
                row = []
                tile = z[:, :, i:i + self.tile_latent_min_size, :]
                tile = self.post_quant_conv(tile)
                decoded = self.decoder(tile)
                vae_scale_factor = decoded.shape[-1] // tile.shape[-1]
                row.append(decoded)
                rows.append(row)
            result_rows = []
            for i, row in enumerate(rows):
                result_row = []
                for j, tile in enumerate(row):
                    # blend the above tile and the left tile
                    # to the current tile and add the current tile to the result row
                    if i > 0:
                        tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                    if j > 0:
                        tile = self.blend_h(row[j - 1], tile, blend_extent)
                    result_row.append(
                        self.blend_h(
                            tile[:, :, :row_limit, w * vae_scale_factor:],
                            tile[:, :, :row_limit, :w * vae_scale_factor],
                            tile.shape[-1] - w * vae_scale_factor))
                result_rows.append(torch.cat(result_row, dim=3))

            dec = torch.cat(result_rows, dim=2)
            if not return_dict:
                return (dec, )

            return DecoderOutput(sample=dec)

        self.vae.tiled_decode = tiled_decode.__get__(self.vae, AutoencoderKL)

        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(prompt, height, width, callback_steps,
                          negative_prompt, prompt_embeds,
                          negative_prompt_embeds)
        self.blend_extend = width // self.vae_scale_factor // 32

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            cross_attention_kwargs.get('scale', None)
            if cross_attention_kwargs is not None else None)
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        num_warmup_steps = len(
            timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat(
                    [latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(
                    latent_model_input, t)

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    return_dict=False,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (
                        noise_pred_text - noise_pred_uncond)

                if do_classifier_free_guidance and guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(
                        noise_pred,
                        noise_pred_text,
                        guidance_rescale=guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred,
                    t,
                    latents,
                    **extra_step_kwargs,
                    return_dict=False)[0]

                # call the callback, if provided
                condition_i = i == len(timesteps) - 1
                condition_warm = (i + 1) > num_warmup_steps and (
                    i + 1) % self.scheduler.order == 0
                if condition_i or condition_warm:
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)
                latents = self.blend_h(latents, latents, self.blend_extend)
            latents = self.blend_h(latents, latents, self.blend_extend)
            latents = latents[:, :, :, :width // self.vae_scale_factor]

        if not output_type == 'latent':
            image = self.vae.decode(
                latents / self.vae.config.scaling_factor, return_dict=False)[0]
            image, has_nsfw_concept = self.run_safety_checker(
                image, device, prompt_embeds.dtype)
        else:
            image = latents
            has_nsfw_concept = None

        if has_nsfw_concept is None:
            do_denormalize = [True] * image.shape[0]
        else:
            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]

        image = self.image_processor.postprocess(
            image, output_type=output_type, do_denormalize=do_denormalize)

        # Offload last model to CPU
        if hasattr(
                self,
                'final_offload_hook') and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept)